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Faculty of Business and Law

ACFI3308 Report (Assessment 1) Assignment Brief

Module Title

Financial Econometrics

Assignment Number

1

Module Code

ACFI3308

Assignment Title

Report

Assignment Weighting

40%

Assignment Release Date:

7 November 2023

Submission Date/Time:

14/12/2023 12:00 (noon)

Assessment Information – What you need to do

This is an individual assignment.

You are the financial analyst for F&H Capital, a boutique investment firm located in New York.

Your main task is to track US equities and make recommendations to the trading departments.

You are tasked to provide an “Equity Research Report” on a portfolio of 20 US equities based on a defined investment strategy.

The firm uses two asset pricing models to assess securities' return and risk characteristics. These are the Capital Asset Pricing Model (CAPM) and the Fama-French Three-Factor model.

The empirical CAPM is specified as follows:

Rit  − RFT   = a + β1 (RMKT  − RFT )                                                                   (1)

where:

β1  is the beta on the market portfolio (S&P 500)

α is the portfolio's alpha (intercept) and measures the underperformance or outperformance level

Rit  is the return on the (equal-weighted) portfolio

RFT  is the return on the risk-free rate at time t

Rit  − RFT  is the excess return on portfolio (Rit ) over the risk-free rate (RF ) at time t

RMKT  − RFT  is the excess return on the market portfolio (S&P 500) over the risk-free rate (RF )  at time t.

The empirical Fama-French Three-Factor (FF3) model is specified as follows:

Rit  − RFT   = a + β1 (RMKT  − RFT) +   β2SMBt    + β3 HMLt                                        (2)

where:

Rit  − RF  is the excess return on portfolio (Rit ) over the risk-free rate (RF ) at time t

RMKT  − RFT  is the excess return on the market portfolio (S&P 500) over the risk-free rate (RF )  at time t.

SMBt  = Excess return of small cap over high cap firms at time t

HMLt  =Excess return of value stock over growth stock at time t

α is the  portfolio's  alpha  (intercept)  and  measures  the  underperformance  or  outperformance level.

β1 to β3 are coefficients to be estimated.

The details of your report are itemised below.

SECURITY SELECTION AND PORTFOLIO CONSTRUCTION

1.  Using an investment strategy, select twenty (20)  US equities from Refinitiv Eikon or Yahoo Finance. Explain how your investment strategy was implemented in the stock picking.

2. For each security, download the close price (or adjusted close price) series for the period 1 January 2018 to 31 October 2023.

3. Transform the monthly price series to a return series using the log return formula.

4. Construct an equal-weighted portfolio return from the 20 stocks you selected.

5. Set the portfolio rebalancing to one (1) month. [10 MARKS]

PORTFOLIO RETURN AND STATISTICS

6. Plot the monthly return series of your equal-weighted portfolio.

7. Generate the following summary statistics for the equal-weight portfolio return. (Mean, Median, Standard Deviation, Minimum, Maximum, Skewness, Kurtosis)

8. Discuss the portfolio summary statistics on profitability and volatility. [10 MARKS]

TIME SERIES REGRESSION

9. From theFama-French website, download the monthly Fama-French-Three factor series directly into R from 1 January 2015 to 30 June 2023.

10.   Estimate a CAPM regression using equation (1) above with the excess return on the equal-weighted portfolio as the dependent variable.

11.    Estimate the Fama-French Three-Factor (FF3) regression using equation (2) above and the excess return on the equal-weighted portfolio as the dependent variable.

12.    Interpret the coefficients on the two asset pricing models and test for their statistical significance. Comment on the R-squared and F-statistics for the two asset pricing models.

13.   Test for the unbiasedness of the CAPM regression model and discuss whether your model violates this theorem.

14.   Test for serial correlation (autocorrelation) and heteroskedasticity in your CAPM and FF3 models. Comment on the result and how this impacts your statistical inferences of the coefficients.

15.    Re-estimate the CAPM and FF3 regression with heteroskedastic and autocorrelation consistent (HAC) standard errors. Comment on the difference between this result and the one estimated in step 11. [40 MARKS]

ROLLING REGRESSION MODEL

16.   This time, re-estimate the FF3 model using a rolling regression model in equation (2). The rolling regression should be based on a 36-month lookback window. The regression should be estimated using the heteroskedasticity and autocorrelation consistent (HAC) standard errors.

17.   Plot the rolling betas (coefficients) on your portfolio's FF3 factors and the alpha (intercept). This means that you must plot the rolling window graphs for Rmt, SMBt, HMLt  and the alpha (Intercept) term.

18.   Discuss the pattern observed in these graphs and  how they differ from those reported for FF3 in the previous section.

19.   Using  your  alpha  and  beta  plots  on  Mkt-Rf,  comment  on  the  impact  of  the  COVID-19 pandemic on your portfolio’s performance. [30 MARKS]

Criteria for Assessment - How you will be marked

The marks will be allocated as follows:

SECURITY SELECTION AND PORTFOLIO CONSTRUCTION [10 MARKS]

.    Rationale provided for the securities selected.          [5 marks]

.    Details on how the portfolio is constructed [5 marks]

PORTFOLIO RETURN AND STATISTICS [10 MARKS]

.    Tabulation of summary statistics [5 marks]

.    Discussion of economic rationale [5 marks]

TIME SERIES REGRESSION [40 MARKS]

.    Appropriate regression estimation outputs [5 marks]

.    Interpretation and discussion of the regression estimates [10 marks]

.    Comparison of the two asset pricing models [10 marks]

.    Unbiasedness of OLS /autocorrelation/heteroskedasticity [10 marks]

.    Test of and discussion of heteroskedasticity/autocorrelation [5 marks]

ROLLING REGRESSION MODEL [30 MARKS]

.    Appropriate regression estimation outputs [10 marks]

.    Discussion of the rolling regression graphs [10 marks]

.    Appropriate discussion of the impact of the Covid-19 pandemic on your portfolio and choice of investment strategy [10 marks]

FORMAT OF REPORT [10 MARKS]

Executive summary, introduction, logical structure, and appropriate referencing.

Most of the marks will be given to students who produce evidence of:

.    A good understanding of asset pricing models and time series analysis.

.    A good understanding of time series regression analysis and forecasting.

.    Critical thinking in the interpretation of the findings.

A mark rubric (ACFI3308 Report Rubric) is also provided in the assessment folder.

Further information on University mark descriptorscan be found here.

Assessment Details

The written report should be 1,000 words (plus or minus 10%).

There will be a penalty of a deduction of 10% of the mark for work exceeding the word limit by 10% or more.

The word limit includes quotations and citations but excludes tables, figures, the references list, executive summary, and appendices.

Data analysis and model estimations are to be carried out using the R Statistical programme.

Using alternative packages/software will lead to a penalty of 15% of the total grade for this assessment.

This assignment is designed to assess the following learning outcomes:

(I)          Evaluate the defining characteristics of the various types of stochastic processes as well as distinguish alternative financial econometric methodologies and effectively select the most appropriate one for the nature of the available data series.

(II)        Perform and appraise business and economic forecasts using various econometrics techniques.

(III)       Effectively communicate orally or in writing conceptually challenging concepts and ideas.

How to Submit your Assessment

Two separate files must be submitted for this assessment.

The assessment (Report) must be submitted by 12:00 noon (GMT/BST) on 14/12/2023 via

Turnitin on the ACFI3308 module shell in LearningZone. No paper copies are required. You can access the submission link through the module web.

The R Script and data used for the assessment must be submitted separately using the OneDrive link below on 15/12/2023.

OneDrive R Script and Data Submission Link

No paper copies are required. You can access the submission link through the module web.

.    Your coursework will be given a zero mark if you do not submit a copy through Turnitin. Please take care to ensure that you have fully submitted your work.

.    Please ensure that you have submitted your work using the correct file format, unreadable files will receive a mark of zero. The Faculty accepts Microsoft Office and PDF documents,   unless otherwise advised by the module leader.

.    All work submitted after the submission deadline without a valid and approved reason will be subject to theUniversity regulationson late submissions.

o If an assessment is submitted up to 24 hours late the mark for the work will be capped at the   pass mark of 40 per cent for undergraduate modules or 50 per cent for postgraduate modules

o If an assessment is submitted beyond 24 hours late the work will receive a mark of zero per cent

o The above applies to a student’s first attempt at the assessment. If work submitted as a reassessment of a previously failed assessment task is submitted later than the deadline the work will immediately be given a mark of zero per cent

o If an assessment which is marked as pass/fail rather than given a percentage mark is submitted later than the deadline, the work will immediately be marked as a fail

.    The University wants you to do your best. However, we know that sometimes events happen which mean that you can’t submit your coursework by the deadline – these events should be beyond your control and not easy to predict.  If this happens, you can apply for an extension  to your deadline for up to five working days, or if you need longer, you can apply for a deferral, which takes you to the next assessment period (for example, to the re-sit period following the main Assessment Boards). You must apply before the deadline for your assessment. You will find information about applying forextensions and deferrals here.

.    Students MUST keep a copy and/or an electronic file of their assignment.

.    Checks will be made on your work using anti-plagiarism software and approved plagiarism checking websites.

Return of Marked Work

You can expect to have feedback returned to you on 13/01/2024(15 working days). If for any reason there is a delay, you will be kept informed. Marks and feedback will be provided online. It  is important that you access the feedback you receive as this will help to make improvements to   your later work, you can request a meeting with your Module Leader or Personal Tutor to discuss your feedback in more detail.

Marks will have been internally moderated only, and will therefore be provisional; your mark will be formally agreed later in the year once the external examiner has completed their review.

More information on assessment and feedbackcan be found here.

Academic Integrity and Generative AI Use

In submitting a piece of work for assessment it is essential that you understand the University's  requirements for maintaining academic integrity and ensure that the work does not contravene University regulations. Some examples of behaviour that would not be considered acceptable include plagiarism, re-use of previously assessed work, collusion with others and purchasing your assignment from a third party. For more information on academic offences, bad academic practice, and academic penalties, please readchapter four of our academic regulations.

Generative AI tools may be used selectively for this assessment.

Any use  of  generative  AI  needs  to  be  appropriately  acknowledged.  Students  should  add  a statement explaining which technologies were used, how they were used, and how this output was then used to complete the assignment. Direct use of outputs should be cited.

Examples of a student acknowledgement statement are shown below:

Statement of acknowledgment 1 - generative AI used when module leader allows it.

I    have used    (list     all    AI    system     (s)    used     and    links e.g.,    Google’s     Bard, https://bard.google.com) to (provide details of how you have used generative artificial intelligence e.g., to breakdown some of the concepts taught on the module). The prompt (s) I have used are (include the prompt (s) e.g., you are an enthusiastic tutor who is also an experienced   economist,   help   me    understand   the   concept   of   game   theory   with explanations  and  examples).  What  was  generated  from  these  prompts  was  used  to (explain how they were used in your submission, e.g., develop a section on game theory exemplars).

You can find the library guide on generative AI use here -https://library.dmu.ac.uk/genai

Academic Support and Your Well-being

Referencing is the process of acknowledging other people’s work when you have used it in your assignment or research. It allows the reader to locate your source material as quickly and easily as possible so that they can read these sources themselves and verify the validity of your arguments. Referencing provides the link between what you write and the evidence on which it is based.

You identify the sources that you have used by citing them in the text of your assignment (called citations or in-text citations) and referencing them at the end of your assignment (called the reference list or end-text citations). The reference list only includes the sources cited in your text. The main referencing guide can befound hereand includes information on the basics of referencing and achievinggood academic practice. It also has tabs for the specific referencing styles depending on whether you requireHarvard style used in businessorOSCOLA style used by the Law school.

The University has a wealth of support services available to students; further information can be obtained fromStudent Gateway, theStudent Advice Centre,Library and Learning Servicesand,   most importantly, your Personal Tutor. If you are struggling with your assessments and/or deadlines please do seek help as soon as possible so that appropriate support and guidance can be identified and put in place for you. More information can be found on theHealthy DMU pages.